Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

The Art and Science of DDS Data Modeling

The Data Distribution Service (DDS) is a standard for ubiquitous, interoperable, secure, platform independent, and real-time data sharing across network connected devices. DDS is today used and recommended in a large class of application domains, such as Industrial Internet of Things (IIoT), Defense and Aerospace, Transportation, Robotics, Energy, and Medical. Differently from traditional message-centric technologies, DDS is data-centric – the accent is on seamless (user-defined) data sharing as opposed to message delivery. Therefore, when embracing DDS and data-centricity, data modeling becomes a key step in the design of a distributed system. This presentation will (1) explain the role and scope of data modeling in DDS, (2) introduce the techniques at the foundation of effective and extensible Data Models, and (3) summarize the most common DDS Data Modeling Idioms.

The Art and Science of DDS Data Modeling

2.
CopyrightPrismTech,2014
PrismTech
A Recurring Question
• People new to DDS recurrently ask a question: what are the techniques and
patterns that we can use to design DDS-based Systems?
• My answer is usually: Start with the powerful tools and techniques provided
by relational data modelling and then add some DDS-specific spice
• I’ve come to the conclusion that many people are not very familiar with
relational data modelling, or perhaps it is way too long that they have
studied/reviewed these concepts
• This webcast, will provide a relatively well introduction to the relational
data model

4.
CopyrightPrismTech,2014
PrismTech
Relational Model
• Introduced by Edward Codd in 1970 as a way of representing data models
for Data Bases
• Simple and Elegant: A database becomes a collections of one or more
relations where each relation is a table with rows and columns

5.
CopyrightPrismTech,2014
PrismTech
Relation
• The relation is the construct used representing data in the relational model,
it consists of two dimensional table
• The columns of a relation are called attributes
• The name of the relation along with the set of attributes defines the relation
schema
• The rows of the relation, other than the header containing the attribute
names, are called tuples

7.
CopyrightPrismTech,2014
PrismTech
Tuples
• An instance of a relation is a set of tuples (records) in which each tuple has the same
number of fields as in the relation schema.
• A relation’s instance can be visualised as table where each tuple is a row and all
rows have the same number of fields (columns)
• Notice that rows are all different. This is a requirement of the relational model, as a
relation instance is a collection of unique tuples (or rows)
sid name age gpa
1234 Peter Parker 21 4.0
2345 Tony Stark 15 4.0
3456 Bruce Wayne 23 3.5

8.
CopyrightPrismTech,2014
PrismTech
Cardinality and Degree
• The cardinality of a relation R is defined as the number of tuples belonging
to the relation
• The degree, or arity, of a relation R is defined as the number of its fields

9.
CopyrightPrismTech,2014
PrismTech
Keys
• The key of a relation is a set of fields that uniquely identifies a tuple
• A superkey is a set of attributes that includes the primary key
• Example:
- The sid field is the key for the Students relations
sid name age gpa
1234 Peter Parker 21 4.0
2345 Tony Stark 15 4.0
3456 Bruce Wayne 23 3.5

13.
CopyrightPrismTech,2014
PrismTech
Data Distribution Service (DDS)
• DataWriters and DataReaders are
automatically and dynamically
matched by the DDS Discovery
• A rich set of QoS allows to control
existential, temporal, and spatial
properties of data

15.
CopyrightPrismTech,2014
PrismTech
Topic
• A Topic defines a domain-wide information’s class
• A Topic is defined by means of a (name, type,
qos) tuple, where
- name: identifies the topic within the domain
- type: is the programming language type associated
with the topic. Types are extensible and evolvable
- qos: is a collection of policies that express the non-
functional properties of this topic, e.g. reliability,
persistence, etc.
Topic
Type
Name
QoS

16.
CopyrightPrismTech,2014
PrismTech
Topic and Instances
• As explained in the previous slide a topic defines a class/type of information
• Topics can be defined as Singleton or can have multiple Instances
• Topic Instances are identified by means of the topic key
• A Topic Key is identified by a tuple of attributes -- like in databases
• Remarks:
- A Singleton topic has a single domain-wide instance
- A “regular” Topic can have as many instances as the number of different key
values, e.g., if the key is an 8-bit character then the topic can have 256 different
instances

22.
CopyrightPrismTech,2014
PrismTech
UML Data Modelling
• A subset of UML can be used to model Data Models
• The resulting model can be easily translated into a relational model and the used
in a DBMS or DDS
• The allowed subset of UML are:
- Classes (with only attributes)
- Associations
- Association Classes
- Subclasses
- Composition and Aggregation
• UML Data Models can be automatically translated into relational model as far as
each “regular” class defines a primary key

23.
CopyrightPrismTech,2014
PrismTech
Class
• A UML class is mapped to a relation that has the same name of the class,
shares its key and attributes
sid: int
name: string
age: int
gpa: ﬂoat
Student
Student(sid, name, age, gpa)

24.
CopyrightPrismTech,2014
PrismTech
Association
• By default association can be mapped as follows, yet, depending on the
multiplicity of the association different mappings may be possible/desirable
• The key definition in the association depends on the multiplicity
C1(K1, O1)
C2(K2, O2)
A(K1,K2)K1: PK
O1
C1
K2: PK
O2
C2
A

25.
CopyrightPrismTech,2014
PrismTech
1-to-many Association
There are two ways of mapping a 1-to-many association to the relational
model
M1 Use a relation to capture the association
M2 Embed the association on the many side of the association
M1 C1(K1, O1), C2(K2, O2), A(K1, K2)
M2 C1(K1, O1), C2(K2, O2, K1)
K1: PK
O1
C1
K2: PK
O2
C2A
0..1 *

31.
CopyrightPrismTech,2014
PrismTech
Composition and Aggregation
• The precondition to easily map composition to the relational model is for
the part not to have a key
K: PK
W
Whole
P
Part
Whole(K, W) Part(P, K)
• When mapping aggregation (unfilled diamond), the key K on the Part
should have a domain that allows for null values

32.
CopyrightPrismTech,2014
PrismTech
Summing Up
• A subset of UML can be used to model relational data models
• The mapping rules can be used to help translating existing Object Oriented
data models into their relational counter-part

34.
CopyrightPrismTech,2014
PrismTech
Why Relation Reﬁnement?
• The UML/ER Data Models provide usually a good starting point toward the
data model that we’ll actually use in the system
• The relations implied by the UML/ER Data Model often need to be
normalised and re-organised to address performances and workload criteri
• The goal of relation refinements is to remove redundancy and/or
decompose a relation with smaller relations
• Normal forms provide a way of measuring the amount of redundancy that
may be in our data model

35.
CopyrightPrismTech,2014
PrismTech
Redundancy
• Redundant Storage: Information may be stored multiple times leading to
space, and perhaps time, inefficiencies
• Update Anomalies: If one copy of the redundant information is update this
may create inconsistencies in other copies — unless all copies are updated
at the same time
• Insertion Anomalies: It may not be possible to store some information,
unless some other information is stored as well
• Deletion Anomalies: It may not be possible to delete some information
without loosing som other information as well

36.
CopyrightPrismTech,2014
PrismTech
Decomposition
• Unconsidered decomposition can lead more problems than benefits, thus
when decomposing you always want to ensure that:
- You really need to decompose the relation
- You fully understand the implications of the decomposition (lossless join,
dependency preservation)
• Normal Forms provide good guidelines for relations decompositions as they
guarantees that certain class of problems cannot be introduced
• Notice that decomposition can have a performance impact as it may
lead to an increase in joins

37.
CopyrightPrismTech,2014
PrismTech
Functional Dependencies
• A Functional Dependency (FD) is a kind of Integrity Constraint (IC) that
generalises the concept of a key
• Given a relation R along with two nonempty sets of attributes X and Y in R,
we say that R satisfies the FD X ⟶ Y (X determines Y) if the following holds
for every pair of tuples t1 and t2 in R:
• In other terms, the FD says that if two tuple agree on the set of attributes on
X they also agree on the set of attributes in Y
• Notice that a primary key constraint is a special kind of FD
if t1.X = t2.X then t1.Y = t2.Y

38.
CopyrightPrismTech,2014
PrismTech
Example
• Let’s assume our Student relation now includes a new attribute that measure the
percentile of the student GPA, e.g. which percentage of students has a GPA that is
smaller of equal
• Clearly we have that the percentile attribute functionally depends on gpa, or
equivalently gpa ⟶ percentile
sid name age gpa percentile
1234 Peter Parker 21 4.0 100
2345 Tony Stark 15 4.0 100
3456 Bruce Wayne 23 3.5 75

40.
CopyrightPrismTech,2014
PrismTech
Normal Forms
• Different Normal Forms (NF) exist that provide guidance on how to decompose
relations
• If a relation is in a given normal form then we are guarantees that some
anomalies cannot arise, e.g. update anomaly, etc.
• The normal forms based on functional dependencies are the first normal form
(1FN), second normal form (2FN), third normal form (3NF) and the Boyce-Codd
normal form (BCNF)
• Every relation in BCNF is also in 3NF, every relation in 3FN is also in 2FN and finally
every relation in 2NF is also in 1NF
• The 2NF and 3NF have only historical interest, while the BCNF has important
practical applicability

41.
CopyrightPrismTech,2014
PrismTech
1NF
• A relation is in 1NF if every field contains only atomic values, that is not lists,
or sets

42.
CopyrightPrismTech,2014
PrismTech
Boyce-Codd Normal Form (BCNF)
Let R be a relation, X a subset of attributes of R and a an attribute of R. R is in Boyce-Codd
Normal Form (BCNF) if for every FD: X ⟶ {a} that holds over R, one of the following is true:
• a ∊ X, that is it is a trivial FD, or
• X is a superkey
Intuitively, in a BCNF relation the only nontrivial dependencies are those in which a key
determines some attributes. Each attribute must describe the key, the whole key, and
nothing but the key
key attr 1 attr 2 attr k
Functional Dependencies in BCNF

43.
CopyrightPrismTech,2014
PrismTech
BCNF Decomposition Algorithm
Input: relation R and FDs for R
Output: decomposition of R into BCNF relations with lossless join
Compute Keys for R
Repeat until all relations are in BCNF
Choose a relation Ri with A ⟶ B that violates BCNS
Decompose Ri into R1(A, B) and R2(A, rest)
Compute FDs for R1 and R2
Compute Keys for R1 and R2

44.
CopyrightPrismTech,2014
PrismTech
3NF
Let R be a relation schema, X a subset of attributes of R and a an attribute of R.
R is in Third Normal Form if for every FD: X ⟶ {a} that holds over R, one of the
following is true:
• a ∊ X, that is it is a trivial FD, or
• X is a superkey, or
• a is part of some key for R
The definition of 3NF is similar to that of BCNF, with the difference that a may be
part of a key for R

48.
CopyrightPrismTech,2014
PrismTech
Joins
• Join is one of the most useful operator in relational algebra and is most
commonly used to combine/reassemble information from two or more
relations
• Join is conceptually a cross product followed by a selection and projection

49.
CopyrightPrismTech,2014
PrismTech
Condition Joins
• Condition joins are the most general form of joins. This operation takes a
condition and two relations and is defined as follows:
R ⋈c C = σc(RxS)

50.
CopyrightPrismTech,2014
PrismTech
Equĳoin
• Equijoin is a special case of the Condition Join, where the condition
predicates on attribute equality

51.
CopyrightPrismTech,2014
PrismTech
Natural Join
• A Natural Join is a special Equijoin that operates on all the attributes having
the same name in R and S

55.
CopyrightPrismTech,2014
PrismTech
DDS Speciﬁc Decomposition
• In some instances you may find that a topic (relation) R has two disjoint sets
of attribute X and Y that have conflicting temporal, reliability or durability
requirements
• In this case this relation has to be further decomposed

56.
CopyrightPrismTech,2014
PrismTech
Frequency Mix
• Suppose you have a relation R(K, X,Y) were the set of attributes X changes
far more frequently than the set of attributes Y (e.g. position, vs. velocity)
• In this case you should decompose the relation R into:
• This will reduce the resource usage in your system, e.g. bandwidth as well
as CPU but may introduce consistency issues. If consistency is essential then
coherent updates should be used to atomically update R1 and R2
R1(K, X), R2(K, Y)

57.
CopyrightPrismTech,2014
PrismTech
Reliability Mix
• Suppose you have a relation R(K, X,Y) were the set of attributes Y represent
some soft-state.
• In this case you should decompose the relation R into:
• This decomposition allows to only use reliable distribution for R1 and best-
effort for R2 thus reducing resource usage in the system
R1(K, X), R2(K, Y)

58.
CopyrightPrismTech,2014
PrismTech
Durability Mix
• Suppose you have a relation R(K, X,Y) were the set of attributes X requires
a different durability than the set of attributes Y, e.g. X need sto be
persistent while Y volatile
• In this case you should decompose the relation R into:
• This will reduce the resource usage in your system and reduce the pressure
on the Durability Service
R1(K, X), R2(K, Y)

60.
CopyrightPrismTech,2014
PrismTech
Concluding Remarks
• The relational model provides the right set of tools for designing DDS-based
systems
• DDS Topics are relations and DDS supports a subset of relational algebra to
manipulate these relations (topics)
• The design process is as follows:
- Start modelling your system using the UML Data Modelling subset
- Ensure your model is in BCNF or 4NF — make sure your understand why some
violations are necessary/desirable for your system
- Add QoS to your relations
- Evaluate if further decomposition is required due to QoS mixes — if your data
model is properly normalised

63.
CopyrightPrismTech,2014
PrismTech
coursera.org
• Jennifer Widom, Stanfords, Introduction to Databases
- A very very good course on Databases in general and specifically on relational
data modelling

67.
CopyrightPrismTech,2014
PrismTech
Entities, Attributes and Entity Sets
• An entity is an object in the real world that is distinguishable from other
objects
- e.g. the iPhone, the Samsumg Galaxy Note, etc.
• An entity is described through a set of attributes
• An entity set identifies a collections of similar entities
- e.g., Mobile Phones
• Each attribute associated with an entity set must identify its domain
• An entity has a primary key and potentially several candidate keys

69.
CopyrightPrismTech,2014
PrismTech
Relationships
• A relationship is an association between two or more entities
- e.g., a student is enrolled in a course
• A relationship can have descriptive attribute to record information about a
relationship

70.
CopyrightPrismTech,2014
PrismTech
Mapping
• A relationship Set is mapped to a relation
• The attributes of the resulting relation are:
- the primary key of each participating entity as foreign keys
- descriptive attributes as fields of the relation
• The primary key of the resulting relations depends on arity of the
relationship

72.
CopyrightPrismTech,2014
PrismTech
Mapping
ISA relationships can be mapped into two ways
• Map each entity to a distinct relation
• Create only relations for the concrete types
Notice that while the first approach is always applicable, the second is not